Participant info

age_group N meanAge sdAge nFemale
Children 17 11.32 0.8799 8
Adolescents 29 15.37 1.518 14
Adults 46 22.11 2.36 25

Agency task: Machine selection

Model: Optimal machine choices across trials by condition and age

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: stage_2_acc ~ condition * zTrialOfCond * (zAge) + (condition * 
## Model:     zTrialOfCond | subID)
## Data: banditTask.filtered
## Df full model: 18
##                        Effect df     Chisq p.value
## 1                   condition  1 20.86 ***   <.001
## 2                zTrialOfCond  1 60.59 ***   <.001
## 3                        zAge  1      1.46    .227
## 4      condition:zTrialOfCond  1      0.08    .781
## 5              condition:zAge  1      0.21    .650
## 6           zTrialOfCond:zAge  1      1.46    .226
## 7 condition:zTrialOfCond:zAge  1      0.09    .770
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: stage_2_acc ~ condition * zTrialOfCond * (zAge) + (condition *  
##     zTrialOfCond | subID)
##    Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##   8674.1   8807.6  -4319.1   8638.1    12262 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -16.3075   0.0788   0.1984   0.3889   2.4780 
## 
## Random effects:
##  Groups Name                    Variance Std.Dev. Corr             
##  subID  (Intercept)             2.4576   1.5677                    
##         condition1              0.3558   0.5965   -0.09            
##         zTrialOfCond            0.4269   0.6534    0.77  0.01      
##         condition1:zTrialOfCond 0.2272   0.4766    0.18  0.36  0.13
## Number of obs: 12280, groups:  subID, 92
## 
## Fixed effects:
##                              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                   2.37787    0.17242  13.792  < 2e-16 ***
## condition1                   -0.41517    0.08149  -5.095  3.5e-07 ***
## zTrialOfCond                  0.71264    0.08315   8.571  < 2e-16 ***
## zAge                          0.20570    0.16936   1.215    0.225    
## condition1:zTrialOfCond      -0.01845    0.06823  -0.270    0.787    
## condition1:zAge              -0.03405    0.07434  -0.458    0.647    
## zTrialOfCond:zAge             0.09524    0.07818   1.218    0.223    
## condition1:zTrialOfCond:zAge  0.01863    0.06182   0.301    0.763    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndtn1 zTrlOC zAge   cn1:TOC cnd1:A zTOC:A
## condition1  -0.108                                           
## zTrialOfCnd  0.713 -0.045                                    
## zAge         0.021 -0.003  0.033                             
## cndtn1:zTOC  0.096  0.473 -0.007 -0.001                      
## condtn1:zAg -0.003  0.091 -0.002 -0.096  0.079               
## zTrlOfCnd:A  0.034 -0.003  0.067  0.715 -0.009  -0.014       
## cndt1:TOC:A -0.002  0.078 -0.009  0.131  0.091   0.397  0.044

Plot: Proportion optimal machine selections across age groups and trials

Agency task: Agency decisions

Model: Agency decisions by VoC

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: agency ~ zVoC * zTrialOfCond * zAge + (zVoC * zTrialOfCond | 
## Model:     subID)
## Data: banditTask
## Df full model: 18
##                   Effect df      Chisq p.value
## 1                   zVoC  1 144.39 ***   <.001
## 2           zTrialOfCond  1     3.56 +    .059
## 3                   zAge  1       0.01    .906
## 4      zVoC:zTrialOfCond  1  50.21 ***   <.001
## 5              zVoC:zAge  1     4.17 *    .041
## 6      zTrialOfCond:zAge  1       2.53    .111
## 7 zVoC:zTrialOfCond:zAge  1    9.59 **    .002
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: agency ~ zVoC * zTrialOfCond * zAge + (zVoC * zTrialOfCond |  
##     subID)
##    Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##  26597.3  26746.3 -13280.7  26561.3    28962 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -33.889  -0.549   0.190   0.543  13.837 
## 
## Random effects:
##  Groups Name              Variance Std.Dev. Corr             
##  subID  (Intercept)       1.86467  1.3655                    
##         zVoC              0.45871  0.6773   -0.18            
##         zTrialOfCond      0.23503  0.4848    0.63 -0.02      
##         zVoC:zTrialOfCond 0.09566  0.3093   -0.02  0.47 -0.34
## Number of obs: 28980, groups:  subID, 92
## 
## Fixed effects:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.74203    0.14391   5.156 2.52e-07 ***
## zVoC                    1.42145    0.07444  19.096  < 2e-16 ***
## zTrialOfCond            0.10238    0.05418   1.890  0.05879 .  
## zAge                   -0.01696    0.14368  -0.118  0.90603    
## zVoC:zTrialOfCond       0.32578    0.03909   8.335  < 2e-16 ***
## zVoC:zAge               0.15339    0.07390   2.076  0.03792 *  
## zTrialOfCond:zAge      -0.08631    0.05364  -1.609  0.10759    
## zVoC:zTrialOfCond:zAge  0.12286    0.03816   3.219  0.00129 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) zVoC   zTrlOC zAge   zVC:zTOC zVC:zA zTOC:A
## zVoC        -0.157                                            
## zTrialOfCnd  0.603 -0.002                                     
## zAge         0.003  0.000  0.001                              
## zVC:zTrlOfC -0.005  0.431 -0.219  0.001                       
## zVoC:zAge    0.000  0.015  0.002 -0.161  0.013                
## zTrlOfCnd:A  0.001  0.001  0.012  0.604 -0.005   -0.006       
## zVC:zTrOC:A  0.001  0.013 -0.004 -0.008  0.041    0.427 -0.242

Model: Agency decisions when VoC = 0

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: agency ~ zAge + (1 | subID)
## Data: banditTask.vocZeroTrials
## Df full model: 3
##   Effect df Chisq p.value
## 1   zAge  1  0.00    .961
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: agency ~ zAge + (1 | subID)
##    Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##   4304.7   4323.7  -2149.4   4298.7     4137 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2769 -0.6929  0.3759  0.6286  2.1169 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  subID  (Intercept) 1.24     1.113   
## Number of obs: 4140, groups:  subID, 92
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.246212   0.124404  10.017   <2e-16 ***
## zAge        -0.006025   0.123891  -0.049    0.961    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##      (Intr)
## zAge -0.002

Plot: Sensitivity to the value of choice

Plot: Sensitivity to value of control with continuous age

Summary stats: Sensitivity to value of control

Choice preference task

Choice preference task: summary stats

Model: Choice preference task accuracy

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: correct ~ zDiff * zAge + (zDiff | subID)
## Data: rewardSense.filtered
## Df full model: 7
##       Effect df     Chisq p.value
## 1      zDiff  1 79.85 ***   <.001
## 2       zAge  1      0.78    .376
## 3 zDiff:zAge  1      1.36    .244
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Explicit reward knowledge task

Explicit reward knowledge task: summary stats

Model: Explicit reward knowledge by age and true probabilities

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: error ~ zTrueProb * zAge + (1 | subID)
## Data: explicitKnow.filtered
##           Effect        df         F p.value
## 1      zTrueProb 1, 456.86 21.67 ***   <.001
## 2           zAge  1, 89.88      0.04    .845
## 3 zTrueProb:zAge 1, 456.86      0.36    .548
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Plot: Explicit reward knowledge

---
title: "VoC Analyses Part 2: Regressions"
date: 1/8/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---
```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```
                      
```{r setup, include=FALSE}

# list all packages required for the analysis
list_of_packages <- c("tidyverse", "afex", "pander")

# load all packages 
lapply(list_of_packages, library, character.only = TRUE)

# add theme for plotting
voc_theme <- function () {
  theme(
    panel.border = element_rect(fill = "transparent", color="gray75"),
    panel.background  = element_blank(),
    plot.background = element_blank(), 
    legend.background = element_rect(fill="transparent", colour=NA),
    legend.key = element_rect(fill="transparent", colour=NA),
    line = element_blank(),
    axis.ticks = element_line(color="gray75"),
    text=element_text(family="Avenir"),
    axis.text = element_text(size = 12),
    axis.title = element_text(size = 15),
    title = element_text(size = 15),
    strip.background = element_blank(),
    strip.text = element_text(size=12)
  )
}

color1 = "#00b4d8"
color2 = "#0077b6"
color3 = "#03045e"


#z-score function
scale_this <- function(x){
  (x - mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE)
}

```

# Participant info
```{r participants plot}

#load demographic info
sub_info <- read_csv('data/voc_sub_info.csv') 

# plot histogram of male and female participants
sub_info %>% mutate(whole_age = floor(age)) %>% 
    group_by(subID, gender, whole_age) %>% 
    distinct(subID) %>% 
    ggplot(., aes(x=whole_age, fill=gender)) +
    geom_histogram(binwidth = 1, color="white") +
    scale_fill_manual(name="Sex",
                    labels=c("Female", "Male"),
                    values=c(color1, color2)) +
    scale_y_continuous(breaks = c(2,4,6,8,10),
                   labels = c("2","4","6","8","10"),
                   limits = c(0,10)) +
    xlab("Age") +
    ylab("Count") +
    voc_theme()
```

```{r participant info}

#load demographic info
sub_info <- read_csv('data/voc_sub_info.csv') %>%
    mutate(age_group = case_when(age < 13 ~ "Children",
                                 age > 12.99 & age < 18 ~ "Adolescents",
                                 age > 17.99 ~ "Adults"))

sub_info$age_group <- factor(sub_info$age_group, levels = c("Children", "Adolescents", "Adults"))

# age group information
age_group_info <- sub_info %>%
    group_by(age_group) %>%
    summarize(N = n(), 
              meanAge = mean(age),
              sdAge = sd(age),
              nFemale = sum(gender == "F")
              )

pander(age_group_info)
```


# Agency task: Machine selection
## Model: Optimal machine choices across trials by condition and age
```{r machine choices across trials by age}

# Read in data
banditTask <- read_csv('data/processed/bandit_task.csv') 

#combine with participant age
banditTask <- full_join(banditTask, sub_info, by = c("subID"))

# Filter data to have only trials where people choose agency and exclude trials with 50-50 condition 
banditTask.filtered <- banditTask %>% 
    filter(agency == 1, condition!="bandits5050")

# Scale continuous variables
banditTask.filtered$zAge <- scale_this(banditTask.filtered$age)
banditTask.filtered$zTrialOfCond <- scale_this(banditTask.filtered$trialOfCond)

# Mixed-effects logistic regression model
correct_byConditionTrialAge.mixed <- mixed(stage_2_acc ~ condition*zTrialOfCond*(zAge) + (condition*zTrialOfCond|subID), 
                data = banditTask.filtered,
                family = binomial, 
                method = "LRT",
                control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e6)))

#display model stats
correct_byConditionTrialAge.mixed 
summary(correct_byConditionTrialAge.mixed)
```

## Plot: Proportion optimal machine selections across age groups and trials
```{r plot bandit choices across trials, width = 7, height = 4, unit = "in"}

banditTaskSubMeans <- banditTask %>%
    mutate(block = floor((trial-1)/21) + 1) %>%
    filter(agency==1, condition!="bandits5050") %>% 
    group_by(condition, block, age_group, subID) %>% 
    summarize(pctCorrect = mean(stage_2_acc))

banditTaskMeans <- banditTaskSubMeans %>%
    group_by(condition, block, age_group) %>% 
    summarize(pctCorr = mean(pctCorrect),
              se = sd(pctCorrect)/sqrt(n()))

machineSelectionPlot <- ggplot(banditTaskMeans, aes(x=block, y=pctCorr, color=condition)) +
    facet_wrap(~age_group) +
    geom_point(size = 3) +
    geom_jitter(data = banditTaskSubMeans,  aes(x = block, y = pctCorrect, color=condition), size = .5) +
    geom_smooth(method = "lm", aes(fill = condition)) +
    geom_hline(yintercept = .5, linetype="dashed") +
    ylab("Proportion Optimal Machine Selections") +
    xlab("Block") +
    scale_x_continuous(breaks = c(4, 8, 12)) +
    scale_fill_manual(name="Condition",
                      labels=c("70/30",
                               "90/10"),
                      values=c(color1, color3), 
                      guide = guide_legend(reverse=TRUE)) +
    scale_color_manual(name="Condition",
                      labels=c("70/30",
                               "90/10"),
                      values=c(color1, color3),
                     guide = guide_legend(reverse=TRUE)) +
    voc_theme() +
    theme(strip.text = element_text(size=12))
machineSelectionPlot
```


# Agency task: Agency decisions 
## Model: Agency decisions by VoC
```{r voc model}

#scale voc
banditTask$zVoC <- scale_this(banditTask$voc)
banditTask$zTrialOfCond <- scale_this(banditTask$trialOfCond)
banditTask$zAge <- scale_this(banditTask$age)

# predict agency choice from utility of control, trial, linear age
agency_byVOCTrialAge.mixed = mixed(agency ~ zVoC * zTrialOfCond * zAge + (zVoC * zTrialOfCond|subID), 
                        data = banditTask, 
                        family = binomial, 
                        method = "LRT", control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6))) 

#display stats
agency_byVOCTrialAge.mixed
summary(agency_byVOCTrialAge.mixed)
```

## Model: Agency decisions when VoC = 0
```{r voc 0 model}

#filter data
banditTask.vocZeroTrials <- banditTask %>%
    filter(voc == 0)

#scale age
banditTask.vocZeroTrials$zAge <- scale(banditTask.vocZeroTrials$age)

# predict agency choice from utility of control, trial, linear age
agency_vocZero_byAge.mixed = mixed(agency ~  zAge + (1|subID), 
                        data = banditTask.vocZeroTrials, 
                        family = binomial, 
                        method = "LRT", control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e6))) 

#display stats
agency_vocZero_byAge.mixed
summary(agency_vocZero_byAge.mixed)
```

## Plot: Sensitivity to the value of choice
```{r voc plot, fig.height = 4, fig.width = 7, unit = "in"}

VoC_plot_sub_means <- banditTask %>% 
    mutate(taskHalf = case_when(trial < 158 ~ "First Half of Task",
                                trial > 157 ~ "Second Half of Task")) %>%
    group_by(age_group, taskHalf, voc, subID) %>%
    summarize(meanSubAgency = mean(agency, na.rm = T))

VoC_plot_means <- VoC_plot_sub_means %>% 
    group_by(age_group, taskHalf, voc) %>%
    summarize(meanAgency = mean(meanSubAgency, na.rm = T),
              seAgency = sd(meanSubAgency / sqrt(n())))

#plot
VoC_plot <- ggplot(VoC_plot_means, aes(x = voc, y = meanAgency, color = age_group)) +
    facet_wrap(~taskHalf) +
    geom_point(aes(color = age_group)) + 
    geom_errorbar(aes(color = age_group, ymin = meanAgency - seAgency, ymax = meanAgency + seAgency), width = .1) + 
    geom_line(aes(group = age_group)) +
    scale_color_manual(values=c("#84347C", "#B40424", "#EB6D1E"), name = "Age Group") +
    xlab("Value of Choice (VoC)") +
    ylab("Proportion Agency Choices") +
    geom_hline(yintercept = .5, linetype = "dashed") +
    geom_vline(xintercept = 0, linetype = "dashed") +
    voc_theme()
VoC_plot
```

## Plot: Sensitivity to value of control with continuous age 
```{r voc plot continuous age, fig.height = 3.9, fig.width = 3, unit = "in"}

#run model without age to get random effects for each participant
agency_byVOCTrial.glmer = mixed(agency ~ zVoC * zTrialOfCond + (zVoC * zTrialOfCond|subID), 
                        data = banditTask, 
                        family = binomial, 
                        method = "LRT", control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)),
                        return = "merMod") 

#get fixed effect of zVoC
VoC_fixedeff <- as.data.frame(coef(summary(agency_byVOCTrial.glmer)))$Estimate[2]
VoC_int_fixedeff <- as.data.frame(coef(summary(agency_byVOCTrial.glmer)))$Estimate[4]

#get random effects
VoC_effects <- ranef(agency_byVOCTrial.glmer)$subID %>%
    rownames_to_column(var = "subID")

#combine with age
VoC_subEffects <- banditTask %>%
    select(subID, age) %>% 
    unique() %>%
    left_join(VoC_effects, by = c("subID")) %>%
    mutate(zVoCFull = zVoC + VoC_fixedeff, 
           intFull = `zVoC:zTrialOfCond` + VoC_int_fixedeff)

#plot age by VoC effect
VoC_plot_continuousAge <- ggplot(VoC_subEffects, aes(x = age, y = zVoCFull)) +
    geom_point(color = "#EB6D1E") + 
    geom_smooth(method = "lm", color = "#84347C", fill = "#84347C") +
    voc_theme() + 
    xlab("Age") +
    ylab("VoC Effect") 
VoC_plot_continuousAge

#plot age by VoC x trial effect
VoC_plot_continuousAgeTrial <- ggplot(VoC_subEffects, aes(x = age, y = intFull)) +
    geom_point(color = "#EB6D1E") + 
    geom_smooth(method = "lm", color = "#84347C", fill = "#84347C") +
    voc_theme() + 
    xlab("Age") +
    ylab("VoC x Trial Effect") 
VoC_plot_continuousAgeTrial
```


## Summary stats: Sensitivity to value of control
```{r voc summary stats}

# What proportion of trials did participants choose agency when VoC was 0?
VoC_zero_means_sub <- banditTask %>% 
    filter(voc == 0) %>%
    group_by(subID, age_group) %>%
    summarize(meanSubAgency = mean(agency, na.rm = T))

VoC_zero_means <- VoC_zero_means_sub %>%
    summarize(meanAgency = mean(meanSubAgency, na.rm = T),
              seAgency = sd(meanSubAgency / sqrt(n())))
VoC_zero_means
```


# Choice preference task 
## Choice preference task: summary stats
```{r reward sense summary stats}

# Read in data
rewardSense <- read_csv('data/processed/reward_sensitivity_task.csv') 

#combine with age
rewardSense <- full_join(rewardSense, sub_info, by = c("subID"))

# summary stats for accuracy
overallAcc <- rewardSense %>% 
    group_by(subID) %>% 
    filter(accuracy!=0) %>% 
    summarize(m=mean(correct, na.rm=T)) %>% 
    ungroup() %>% 
    summarize(meanAccuracy = mean(m), stdev = sd(m))
overallAcc

# mean = 76.9%
# stdev = 15.3%
```

## Model: Choice preference task accuracy
```{r bandit choices across by age in post-task assessment}

# first, filter data and rescale variables
rewardSense.filtered <- rewardSense %>%  
    filter(accuracy!=0)

# rescale variables of age and the true probability differences between two displayed bandits 
rewardSense.filtered$zAge <- scale(rewardSense.filtered$age)
rewardSense.filtered$zDiff<- scale(rewardSense.filtered$diff)

# run model
rewardSense.mixed <- mixed(correct~zDiff*zAge + (zDiff|subID), 
                           data= rewardSense.filtered,
                           family = binomial,
                           method = "LRT")
rewardSense.mixed 
```


# Explicit reward knowledge task 
## Explicit reward knowledge task: summary stats
```{r explicit knowledge task}

# Read in data
explicitKnow <- read_csv('data/processed/explicit_knowledge_task.csv') 

#combine with age
explicitKnow <- full_join(explicitKnow, sub_info, by = c("subID"))

explicitKnow %>% 
  group_by(subID, age) %>% 
  summarize(m = mean(error)) %>% 
  ungroup() %>% 
  summarize(meanErr = mean(m, na.rm=T), sd = sd(m,na.rm = T))
```

## Model: Explicit reward knowledge by age and true probabilities
```{r explicit knowledge model}
# predict trial-level error from true probability and age

#re-scale age and zTrueProb
explicitKnow.filtered <- explicitKnow %>%
    select(subID, age, trueProb, response, error) %>%
    drop_na()

explicitKnow.filtered$zAge <- scale(explicitKnow.filtered$age)
explicitKnow.filtered$zTrueProb <- scale(explicitKnow.filtered$trueProb)

# run model
explicitKnow_errorbyTrueProbAge.mixed <- mixed(error ~ zTrueProb*zAge + (1|subID), 
                                               data = explicitKnow.filtered,
                                               method = "S") 
explicitKnow_errorbyTrueProbAge.mixed
```

## Plot: Explicit reward knowledge
```{r plot explicit knowledge}
# plot response by bandit
explicitKnow %>% 
    ggplot(., aes(x=factor(trueProb), y=response, fill=age_group)) +
    geom_boxplot() +
    scale_fill_manual(values = c(color1, color2, color3), name = "Age Group") +
    ylab("Reported Reward Probability") +
    xlab("True Reward Probability") +
    scale_x_discrete(labels = c("10%", "30%", "50%", "70%", "90%")) +
    scale_y_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9), 
                     labels = c("10%", "20%", "30%", "40%", "50%", "60%", "70%", "80%", "90%")) +
    voc_theme()
```
